Signature Prediction Model Research Articles

PROGNOSTIC SIGNIFICANCE OF KAT2A AS A HISTONE ACETYLATION REGULATOR IN DIFFUSE LARGE B‐CELL LYMPHOMA

Diffuse large B-cell lymphoma (DLBCL) is a highly heterogeneous lymphoid neoplasm with variations in the expression of genes and genetic alterations. Histone acetylation is one of the epigenetic alterations associated with lymphoma pathogenesis. Among the many types of cell death, ferroptosis has distinct properties and has been found to be associated with physical conditions as well as various cancers. Using consensus clustering, we categorized DLBCL patients based on histone acetylation regulators and ferroptosis-related genes (HFGs) expression. LASSO Cox regression was used to calculate risk scores. Kaplan-Meier survival analysis, univariate and multivariate Cox regression analysis, and ROC curve analysis were performed. qRT-PCR was used to validate the expression of four HFGs in DLBCL cell lines. According to the study, CREBBP had the highest mutation frequency. Based on CREBBP mutations, patients were divided into mutant and wild groups, and several histone acetylation regulators were overexpressed in the mutant group. To demonstrate the potential malignant mechanisms of HFGs in DLBCL development and their prognostic value. A significant positive self-correlation was observed in HAGs and FRGs. A negative correlation was found between KAT2A, the HA writer, and the majority of FRGs (Fig. 1A). 36 genes were selected (p < 0.05) by univariate regression correlations with clinical prognosis. Consensus clustering was used to categorize the expression of DLBCL patients. K-M survival analysis showed that HFcluster B had a worse prognosis. Based on HFGs, we developed a high-quality and high-accuracy gene signature prediction model. Following LASSO Cox regression, a four-gene signature model was identified. Patients with DLBCL in the high-risk group had a significantly shorter OS (p < 0.001, Figure 1B). ROC curves, univariate and multivariate Cox regression analyses also revealed that riskScore served as an independent indicator for patients with DLBCL (p < 0.001). Analyses revealed that molecular mechanisms mediate histone acetylation and ferroptosis. The results of PCR in CD19+ B cells and DLBCL cell lines also confirmed the accuracy of the model. Due to its close relationship to ferroptosis-related genes, KAT2A was explored in greater depth. Further study revealed that KAT2A was significantly correlated with age (p = 0.028) and IPI (p = 0.016). In univariate analysis, KAT2A was an independent indicator of DLBCL patients (p < 0.001). The expression level of KAT2A was negatively correlated with OS in DLBCL patients (p = 0.003). Keywords: aggressive B-cell non-Hodgkin lymphoma, diagnostic and prognostic biomarkers No conflicts of interests pertinent to the abstract.

Upregulated Immunogenic Cell-Death-Associated Gene Signature Predicts Reduced Responsiveness to Immune-Checkpoint-Blockade Therapy and Poor Prognosis in High-Grade Gliomas.

Background: Immunogenic cell death (ICD) has emerged as a potential mechanism mediating adaptive immune response and tumor immunity in anti-cancer treatment. However, the signature of ICD in high-grade gliomas (HGGs) remains largely unknown, and its relevance to immunotherapies is still undetermined. The purpose of this study is to identify ICD-associated genotypes in order to explore their relevance to tumor immunity, patient prognosis and therapeutic efficacy of immune checkpoint blockade (ICB) therapy in HGGs. Methods: Bulk RNA-seq data and clinical information on 169 and 297 patients were obtained from the Cancer Genome Atlas (TCGA) and China Glioma Genome Atlas (CGGA), respectively. The functional enrichment and characterization of ICD genotyping were detected, and the ICD prognostic signature prediction model was constructed using least absolute shrinkage and selection operator (LASSO) regression. The responsiveness to immunotherapy was predicted according to the scoring of the ICD prognostic signature. Results: The HGG patients with high ICD gene signature (C1) showed poor outcomes, increased activity of immune modulation and immune escape, high levels of immune-checkpoint markers, and HLA-related genes, which may explain their reduced response to ICB immunotherapy. A gene set of the ICD signature, composing FOXP3, IL6 LY96, MYD88 and PDIA3, showed an independent prognostic value in both the TCGA and the CGGA HGG cohort. Conclusions: Our in silico analyses identified the ICD gene signature in HGGs with potential implications for predicting the responsiveness to ICB immune therapy and patient outcomes.

Identification of Histone Acetylation Regulator KAT2A As a Potential Prognostic Biomarker in Diffuse Large B-Cell Lymphoma

Introduction Diffuse large B-cell lymphoma (DLBCL) is a highly heterogeneous lymphoid neoplasm with variations in the expression of genes and genetic alterations. Epigenetic alterations are associated with lymphoma pathogenesis, one of which is histone acetylation. Ferroptosis is a novel type of cell death with distinct properties and recognizing functions involved in physical conditions or various cancers. However, the functions of histone acetylation and ferroptosis in DLBCL remain unclear. Our study assesses the significance and mechanisms of ferroptosis-related genes (FRG) and histone acetylation regulators (HAG). Methods Using consensus clustering, we categorized DLBCL patients based on histone acetylation regulators and ferroptosis-related genes (HFGs) expression. The WGCNA identified genes and mechanisms that affect HF clusters. LASSO Cox regression was used to calculate risk scores. To assess the accuracy of this model, Kaplan-Meier survival analysis, univariate and multivariate Cox regression analysis, and ROC curve analysis were performed. qRT-PCR was used to validate the expression of four HFGs in DLBCL cell lines. Results A total of 38 HA regulators were evaluated for mutations. According to the study, CREBBP had the highest mutation frequency. Based on CREBBP mutations, patients were divided into mutant and wild groups, and several histone acetylation regulators were overexpressed in the mutant group. There was a high rate of copy number variation (CNV) in some genes. To demonstrate the potential malignant mechanisms of HFGs in DLBCL development and their prognostic value. A significant positive self-correlation was observed in HAGs and FRGs. A negative correlation was found between KAT2A, the HA writer, and the majority of FRGs (Fig. 1A). 36 genes were selected (P < 0.05) by univariate regression correlations with clinical prognosis. Consensus clustering was used to categorize the expression of DLBCL patients. K-M survival analysis showed that HFcluster B had a worse prognosis. To investigate the underlying mechanisms, WGCNA was used to divide genes into different modules. Afterwards, we selected the blue and green modules to analyze their functions. KEGG enrichment emphasized oncological pathways like PI3K-Akt, focal adhesion, Hippo, and MAPK signaling pathway. Based on HFGs and their corresponding signatures, we developed a high-quality and high-accuracy gene signature prediction model. Following LASSO Cox regression, a four-gene signature model was identified. Patients with DLBCL in the high-risk group had a significantly shorter OS (P < 0.001, Fig. 1B). ROC curves, univariate and multivariate Cox regression analyses also revealed that riskScore served as an independent indicator for patients with DLBCL (P < 0.001). GSEA enrichment analysis identified JAK-STAT, P53, and APOPTOSIS as the most differentially enriched genes for malignant mechanisms. Analyses revealed that molecular mechanisms mediate histone acetylation and ferroptosis. The results of PCR in CD19+ B cells and DLBCL cell lines also confirmed the accuracy of the model. Due to its close relationship to ferroptosis-related genes, KAT2A was explored in greater depth. Further study revealed that KAT2A was significantly correlated with age (P = 0.028) and IPI (P = 0.016). In univariate analysis, KAT2A was an independent indicator of DLBCL patients (P < 0.001). The expression level of KAT2A was negatively correlated with OS in DLBCL patients (P = 0.003). Conclusions There were significant correlations among these HFGs, indicating their potential role in DLBCL development and predictive value for prognosis. Additionally, our study shows that KAT2A is associated with prognosis in patients with DLBCL. Further study of the mechanisms of KAT2A in DLBCL may help clinicians to individualize the treatment of this patient population. Keywords: histone acetylation, ferroptosis, mechanism, DLBCL Figure 1View largeDownload PPTFigure 1View largeDownload PPT Close modal

A CT-based radiomics nomogram for the differentiation of pulmonary cystic echinococcosis from pulmonary abscess

The purpose of this study was to establish a clinical prediction model for the differential diagnosis of pulmonary cystic echinococcosis (CE) and pulmonary abscess according to computed tomography (CT)-based radiomics signatures and clinical indicators. This is a retrospective single-centre study. A total of 117 patients, including 53 with pulmonary CE and 64 with pulmonary abscess, were included in our study and were randomly divided into a training set (n = 95) and validation set (n = 22). Radiomics features were extracted from CT images, a radiomics signature was constructed, and clinical indicators were evaluated to establish a clinical prediction model. Finally, a model combining imaging radiomics features and clinical indicators was constructed. The performance of the nomogram, radiomics signature and clinical prediction model was evaluated and validated with the training and test datasets, and then the three models were compared. The radiomics signature of this study was established by 25 features, and the radiomics nomogram was constructed by using clinical factors and the radiomics signature. Finally, the areas under the receiver operating characteristic curve (AUCs) for the training set and test set were 0.970 and 0.983, respectively. Decision curve analysis showed that the radiologic nomogram was better than the clinical prediction model and individual radiologic characteristic model in differentiating pulmonary CE from pulmonary abscess. The radiological nomogram and models based on clinical factors and individual radiomics features can distinguish pulmonary CE from pulmonary abscess and will be of great help to clinical diagnoses in the future.

Nomogram Based on CT Radiomics Features Combined With Clinical Factors to Predict Ki-67 Expression in Hepatocellular Carcinoma.

ObjectivesThe study developed and validated a radiomics nomogram based on a combination of computed tomography (CT) radiomics signature and clinical factors and explored the ability of radiomics for individualized prediction of Ki-67 expression in hepatocellular carcinoma (HCC).MethodsFirst-order, second-order, and high-order radiomics features were extracted from preoperative enhanced CT images of 172 HCC patients, and the radiomics features with predictive value for high Ki-67 expression were extracted to construct the radiomic signature prediction model. Based on the training group, the radiomics nomogram was constructed based on a combination of radiomic signature and clinical factors that showed an independent association with Ki-67 expression. The area under the receiver operating characteristic curve (AUC), calibration curve, and decision curve analysis (DCA) were used to verify the performance of the nomogram.ResultsSixteen higher-order radiomic features that were associated with Ki-67 expression were used to construct the radiomics signature (AUC: training group, 0.854; validation group, 0.744). In multivariate logistic regression, alfa-fetoprotein (AFP) and Edmondson grades were identified as independent predictors of Ki-67 expression. Thus, the radiomics signature was combined with AFP and Edmondson grades to construct the radiomics nomogram (AUC: training group, 0.884; validation group, 0.819). The calibration curve and DCA showed good clinical application of the nomogram.ConclusionThe radiomics nomogram developed in this study based on the high-order features of CT images can accurately predict high Ki-67 expression and provide individualized guidance for the treatment and clinical monitoring of HCC patients.

Comprehensive analysis of matrix metalloproteinases and their inhibitors in head and neck squamous cell carcinoma

Objective: This study aimed to explore the association of matrix metalloproteinases (MMPs) and tissue inhibitors of metalloproteinases (TIMPs) with cancer progression and prognosis in head and neck squamous cell carcinoma (HNSCC). Methods: Differentially expressed genes (DEGs) were identified by LIMMA package using R software. The correlation between the expression levels of MMPs and TIMPs in HNSCC cancer samples and adjacent normal tissue samples was performed using Pearson correlation analysis. The Kruskal-Wallis test (H-test) was used to determine the association between the expression level of MMPs/TIMPs and HNSCC clinical stage. The survival result was expressed as a KM curve, and the log-rank test was used for statistical analysis. Lasso regression and multivariate Cox regression analyses were used to examine whether the gene signature based on MMPs and TIMPs was an independent prognostic factor in patients with HNSCC. Results: Among the top 10 most up-regulated genes in HNSCC cancer tissues when compared with normal tissues, six genes belonged to the MMPs. Spearman correlation analysis revealed that only MMP11 and MMP23B were positively correlated with tumor stage. Survival analysis showed that patients with a high expression of MMP14, MMP20, TIMP1, and TIMP4 had a worse prognosis than low expression patients. Additionally, a novel five-gene (MMP3, MMP17, MMP19, MMP24, and TIMP1) signature was constructed and significantly associated with prognosis as an independent prognostic signature. Conclusions: Our data show that the accuracy of a single gene of MMP or TIMP as predictors of progression and prognosis of HNSCC is limited, although some studies have proposed that MMPs act as driving factors for cancer progression. The prediction performance of the five-gene signature prediction model was much better than that of the gene signatures based on every single gene in prognosis prediction.

Cytokine signature and COVID-19 prediction models in the two waves of pandemics

In Europe, multiple waves of infections with SARS-CoV-2 (COVID-19) have been observed. Here, we have investigated whether common patterns of cytokines could be detected in individuals with mild and severe forms of COVID-19 in two pandemic waves, and whether machine learning approach could be useful to identify the best predictors. An increasing trend of multiple cytokines was observed in patients with mild or severe/critical symptoms of COVID-19, compared with healthy volunteers. Linear Discriminant Analysis (LDA) clearly recognized the three groups based on cytokine patterns. Classification and Regression Tree (CART) further indicated that IL-6 discriminated controls and COVID-19 patients, whilst IL-8 defined disease severity. During the second wave of pandemics, a less intense cytokine storm was observed, as compared with the first. IL-6 was the most robust predictor of infection and discriminated moderate COVID-19 patients from healthy controls, regardless of epidemic peak curve. Thus, serum cytokine patterns provide biomarkers useful for COVID-19 diagnosis and prognosis. Further definition of individual cytokines may allow to envision novel therapeutic options and pave the way to set up innovative diagnostic tools.

Radiogenomics Map Reveals the Landscape of m6A Methylation Modification Pattern in Bladder Cancer.

We aimed to develop a noninvasive radiomics approach to reveal the m6A methylation status and predict survival outcomes and therapeutic responses in patients. A total of 25 m6A regulators were selected for further analysis, we confirmed that expression level and genomic mutations rate of m6A regulators were significantly different between cancer and normal tissues. Besides, we constructed methylation modification models and explored the immune infiltration and biological pathway alteration among different models. The m6A subtypes identified in this study can effectively predict the clinical outcome of bladder cancer (including m6AClusters, geneClusters, and m6Ascore models). In addition, we observed that immune response markers such as PD1 and CTLA4 were significantly corelated with the m6Ascore. Subsequently, a total of 98 obtained digital images were processed to capture the image signature and construct image prediction models based on the m6Ascore classification using a radiomics algorithm. We constructed seven signature radiogenomics models to reveal the m6A methylation status, and the model achieved an area under curve (AUC) degree of 0.887 and 0.762 for the training and test datasets, respectively. The presented radiogenomics models, a noninvasive prediction approach that combined the radiomics signatures and genomics characteristics, displayed satisfactory effective performance for predicting survival outcomes and therapeutic responses of patients. In the future, more interdisciplinary fields concerning the combination of medicine and electronics remains to be explored.

A novel protein ubiquitination-related five-gene signature predicts overall survival in patients with lung adenocarcinoma.

Protein ubiquitination has been reported to be involved in many biological processes that affect cancer cell growth or death. In this study, we identified differentially expressed E3s/DUB-related genes associated with the prognosis of lung adenocarcinoma and then constructed an E3s/DUB enzyme signature prediction model for the training group and validated its accuracy for prognosis prediction in the validation group. According to our constructed model, all patients were divided into the high- or low-risk group, and a comparison of the two groups revealed that the high-risk group had poorer survival and higher mortality than the low-risk group. The calculated risk score was also an independent prognostic factor when analyzed together with other clinical factors. To explore the functions of the signature genes, we predicted the substrate proteins with which they interact and then performed enrichment analysis. Interestingly, we found that the signature genes were enriched in multiple treatment resistance and immune-related pathways. Therefore, we continued to analyze immune infiltration in the samples and found a variety of differences in immune cell infiltration. According to our constructed model, these differences in immune cell infiltration may predict different immune statuses after grouping and are associated with worse prognosis in high-risk patients.

Stemness Signature Defined By Logistic Regression Machine Learning and Artificial Intelligence Predicts Outcomes in Diffuse Large B-Cell Lymphoma

Background: Stem and progenitor cell populations sequentially accumulate and lead to cancer through various genetic/epigenetic alterations. The stemness and its association with the tumor microenvironment (TME) have not been studied in diffuse large B-cell lymphoma (DLBCL). Methods: Molecular data from Gene Expression Omnibus of 702 DLBCL patients who received cyclophosphamide, doxorubicin, vincristine, and prednisone combined with rituximab (R-CHOP) were analyzed. A one-class logistic regression (OCLR) machine-learning algorithm was applied to obtain a stemness index (mRNAsi) for each patient and to build molecular stemness-associated genetic signature. A novel stemness molecular signature was established via artificial intelligence to evaluate therapeutic response and prognosis in DLBCL. The significance between the stemness signature and TME cell-infiltrating characteristics was dissected. Results: mRNAsi was biologically significant among DLBCL subtypes and was associated with overall survival (OS). Based on refined 12 stemness-related genes, the stemness molecular signature was able to classify DLBCL patients into high- and low-risk groups. The signature could accurately predict OS and provide a clinically-significant risk stratification in distinguishing DLBCL patients who benefitted from R-CHOP therapy from those with poor outcomes. TME analysis revealed negative correlation between DLBCL stemness origin and features of infiltrating immune cells. Two additional immunotherapy cohorts validated above discoveries in which patients with low-risk score showed significant clinical benefits from PD1/PD-L1 blockade. Conclusions: mRNAsi and stemness molecular signature as new prognostic algorithm was valuable to quantify DLBCL stemness and therapeutic resistance. Such molecular signature and stemness prediction model represent a solid foundation of biologic features for cancer stem cell therapy and immunotherapy in DLBCL. A. survival of patients with high and low mRNAsi. B. According to the stemness molecular signature, two cohorts were defined: high-risk and low-risk cohort. overall survival (OS) in 702 DLBCL patients in the training set. C. AUC for OS at 3 and 5 years in the training cohort to assess prognostic accuracy. D. Expression of 12 stemness signature genes between the germinal B cell and non-germinal B cell subtypes of the training cohort. E. Expression of 12 stemness signature genes between patients at early and advanced stages in the training cohort. F. Proportion of patients with different therapy responses in the high- and low-risk groups of the GSE31312 cohort. G. According to the stratification of the therapy responses, the survival analysis of DLBCL patients. H-I. Survival plots showing the stratification of risk model for each of the individual responses to R-CHOP. J. GSVA enrichment analysis showing the activation states of biological pathways in high- and low-risk groups. K. Abundance of TME-infiltrating cells between high- and low-risk groups. L. Component differences of immune cells between low-risk and high-risk samples analyzed by the CIBERSORT algorithm. M. Based on multivariate Cox regression analysis, riskScore, age, subtype, stage, ECOG, and extranodal sites were integrated to construct a nomogram for predicting patients' prognosis. N-O. Time-dependent receiver operating characteristic (ROC) curve to evaluate the accuracy of the OS nomogram. P-Q. Calibration curves for 3-year OS nomogram model in the training cohort (P), GSE31312 cohort (Q). R. Survival analyses for low- (172 cases) and high- (176 cases) riskScore patient groups in the anti-PD-L1 immunotherapy cohort (IMvigor210 cohort). S. Proportion of patients with different therapy responses in the high- and low-risk groups of the IMvigor210 cohort. T. Difference in riskScore among different clinical response groups in the anti-PD-L1 immunotherapy cohort. U. Kaplan-Meier survival curve according to stratification of the responses to anti-PD-L1 therapy. V. Correlation between each stemness molecular signature gene and each TME infiltration cell type using Pearson correlation analyses. W. Difference in ImmuneScore between high and low ODC1 expression groups. X. Differences in immune-activated pathways between the high and low ODC1 expression groups. Y. Survival analyses for patients with low or high ODC1 expression in the anti-PD-L1 immunotherapy cohort. Figure Disclosures No relevant conflicts of interest to declare.

Identification of Immune-Desert Phenotypes in Head and Neck Squamous Cell Carcinoma With a Nine-Gene Signature Prediction Model Using Bioinformatics&amp;nbsp;Methods

Background: Research into anti-programmed cell death-1 and anti-programmed cell death ligand-1(anti-PD-1/PD-L1) immunotherapy has increased exponentially in the past years. However, immune check inhibitor (ICI) therapies are not effective in all patients. In this study, we explored biomarkers within the TME to identify specific immunophenotypes, and to predict the efficacy of immunotherapy in patients with head and neck squamous cell carcinoma (HNSCC) through bioinformatics analysis. Method: Data on HNSCC patients were derived from TCGA and GEO databases and re-annotated through BLAST analysis. The composition of immune cells in HNSCC was estimated by CIBERSORT and ssGSEA. Weighted correlation network analysis (WGCNA) was used to measure co-expression relationships. The Lasso-logistic model was used to screen biomarkers associated with specific cancer immunophenotypes. A bootstrapping algorithm was used to select optimal cut-off values. Gene function analysis was performed using the Reactome pathway databases. The ESTIMATE algorithm was used to infer the tumor purity. Target drugs which might be used in the immune-desert patient population were screened by a connectivity mapping analysis. Findings: A high degree of similarity in the genes and pathways enriched from the immune-related modules of TCGA HNSCC and GSE65858 datasets was observed. Sixty hub genes were identified through the intersection of two modules to distinguish between immune-desert and non-immune-desert phenotypes. Furthermore, a nine-gene signature prediction model (ALOX5, ARHGDIB, GIMAP1, ITGAL, MAP4K1, RASSF2, SH2D1A, SLAMF6, UCP2) was selected which had a prediction accuracy of 83%, and was related to the efficacy of ICI therapies. Finally, it was found that at least five of these nine genes with negative expression could predict the tumor samples as immune-desert phenotype, and protein kinase C (PKC) activators might be useful drugs for immune-desert patient population in HNSCC. Interpretation: Our nine-gene model may predict the immunophenotype in HNSCC. At least five of these nine genes with negative expression could distinguish immune-desert phenotypes. These findings provide a new strategy to identify the immune state of the TME in HNSCC, and the nine genes may serve as potential biomarkers for clinical applications. The PKC activators might be a potential treatment for immune-desert phenotype tumor. Funding Statement: This work was supported by The National Key Research and Development Program of China (2017YFC1308900), Technological Special Project of Liaoning Province of China (2019020176-JH1/103), Science and Technology Project of Liaoning Province (NO.2013225585), The General Projects of Liaoning Province Colleges and Universities (LFWK201706), National Natural Science Foundation of China (NO.81302023) and China Medical University Youth Backbone Support Plan Project (QGZ2018047). Declaration of Interests: The authors declare no conflict of interests.

Prediction of chemo-response in serous ovarian cancer

BackgroundNearly one-third of serous ovarian cancer (OVCA) patients will not respond to initial treatment with surgery and chemotherapy and die within one year of diagnosis. If patients who are unlikely to respond to current standard therapy can be identified up front, enhanced tumor analyses and treatment regimens could potentially be offered. Using the Cancer Genome Atlas (TCGA) serous OVCA database, we previously identified a robust molecular signature of 422-genes associated with chemo-response. Our objective was to test whether this signature is an accurate and sensitive predictor of chemo-response in serous OVCA.MethodsWe first constructed prediction models to predict chemo-response using our previously described 422-gene signature that was associated with response to treatment in serous OVCA. Performance of all prediction models were measured with area under the curves (AUCs, a measure of the model’s accuracy) and their respective confidence intervals (CIs). To optimize the prediction process, we determined which elements of the signature most contributed to chemo-response prediction. All prediction models were replicated and validated using six publicly available independent gene expression datasets.ResultsThe 422-gene signature prediction models predicted chemo-response with AUCs of ~70 %. Optimization of prediction models identified the 34 most important genes in chemo-response prediction. These 34-gene models had improved performance, with AUCs approaching 80 %. Both 422-gene and 34-gene prediction models were replicated and validated in six independent datasets.ConclusionsThese prediction models serve as the foundation for the future development and implementation of a diagnostic tool to predict response to chemotherapy for serous OVCA patients.Electronic supplementary materialThe online version of this article (doi:10.1186/s12943-016-0548-9) contains supplementary material, which is available to authorized users.

Modeling Time-Evolving Infrared Characteristics for Space Objects With Micromotions

A space object undergoes micromotion dynamics such as spinning, coning, and rolling, in addition to the centroid translation along the trajectory in space. In this work, by taking into account micromotion dynamics and heat transfer, an infrared (IR) signature prediction model for space objects is developed. Results demonstrate that micromotion dynamics results in periodic variation of the projected area to spaceborne sensors, thus influencing the IR characteristics of the space object.

Overlay model for infrared scene generation

A dynamic model of IR missile engagements must combine the output of signature models into a scene of given resolution with a changing viewpoint and moving targets against some background. Some signature prediction models are stand-alone software packages, which currently cannot be dynamically interfaced to a running engagement model. They can be used to conveniently provide an image of an IR target at high resolution at a single viewpoint. An algorithm to solve the radiance map composition problem and combine target, sea, and sky signatures can be thought of intuitively as an idealized imaging radiometer model. We present the derivation of such a model from physical and signal processing considerations and its practical implementation; show that under reasonable assumptions, the radiance integral takes the form of a particularly simple computation; and demonstrate that the model behaves reasonably with translucent targets. The derived methodology provides very high radiometric accuracy with a rigorously controlled error and smooth integration of objects moving through the scene.